Summer 2026 Intern

优缺点分析

角色解读

• Potential to transition into a full-time role as a Machine Learning Engineer or AI Infrastructure Engineer.
• Gain expertise in large-scale distributed systems, GPU computing, and MLOps, opening doors to senior technical roles.
• Opportunity to grow into team leadership positions as you deliver high-impact projects.
• Build and improve production-grade machine learning workflows for training and inference at scale.
• Contribute to AI infrastructure including GPU fleet management, workload orchestration, and model optimization.
• Work with large-scale datasets and distributed systems to enhance Coupang's e-commerce AI capabilities.
• Collaborate with experienced engineers on real features impacting millions of customers.
• Strong foundation in data structures, algorithms, and software engineering principles.
• Proficiency in at least one programming language like Python, Java, or C++.
• Understanding of machine learning concepts and experience with frameworks such as TensorFlow or PyTorch.
• Ability to work in a fast-paced, collaborative environment with cross-functional teams.

申请策略

• Tailor your resume to demonstrate hands-on impact in ML projects, not just coursework.
• Research Coupang's e-commerce AI applications and mention how you align with their mission.
• Emphasize projects involving ML frameworks (PyTorch, TensorFlow) and distributed systems.
• Highlight any prior internship or research in ML, data engineering, or systems.
• Showcase proficiency in Python, Java, or C++ with examples of scalable code.
• Include experience with cloud platforms (AWS, GCP, Azure) or big data tools.
• Deepen knowledge of GPU computing and virtualization concepts.
• Practice building end-to-end ML pipelines using tools like Spark or Kubernetes.

面试指南

• Use the STAR method (Situation, Task, Action, Result) for behavioral questions.
• For technical design questions, define the problem, propose a solution, discuss trade-offs, and consider scalability.
• For optimization questions, start with profiling, identify bottlenecks, then apply techniques like vectorization or parallelization.
• Describe a machine learning project you built from scratch. What challenges did you face?
• How would you debug a model that shows poor inference throughput?
• Explain how you would design a distributed data pipeline for real-time model training.
• What is your experience with GPU virtualization and how does it benefit AI workloads?
• How do you approach optimizing a Python function for performance?